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dinov@umich.edu
Website
Applications:
Behavioral Science, Biological Sciences, Complex Systems, Healthcare Research, Informatics, Social Science
Methodologies:
Bayesian Methods, Computing, Data Mining, Data Visualization, Databases and Data management, Machine Learning, Mathematical and Statistical Modeling, Natural Language Processing, Statistics
Relevant Projects:

Center for Complexity and Self-management of Chronic Disease (CSCD)

University of Michigan Udall Center for Excellence in Parkinson’s Disease

University of Michigan Nutrition Obesity Research Center (MNORC)


Connections:

Big Data to Knowledge (www.BD2K.org)

Probability Distributome (www.Distributome.org)

Statistics Online Computational Resource (www.SOCR.umich.edu)

Ivo D. Dinov

Professor

Computational Medicine and Bioinformatics
Human Behavior and Biological Sciences
Michigan Institute for Data Science (MIDAS)

Professor of Nursing, Director Academic Program, School of Nursing and Professor of Computational Medicine and Bioinformatics, Medical School

Dr. Ivo Dinov directs the Statistics Online Computational Resource (SOCR), co-directs the multi-institutional Probability Distributome Project, and is an associate director for education of the Michigan Institute for Data Science (MIDAS).

Dr. Dinov is an expert in mathematical modeling, statistical analysis, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning.

9.9.2020 MIDAS Faculty Research Pitch Video.

Analyzing Big observational data including thousands of Parkinson's disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.

Analyzing Big observational data including thousands of Parkinson’s disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.